Trace2Policy: From Expert Behavior Traces to Self-Evolving Decision Agents
Quick Answer
Trace2Policy introduces EISR for refining decision rules in compliance tasks, achieving 79.6% accuracy with Python execution, outperforming LLMs by 9.8 percentage points.
Quick Take
Auto-EISR reduces refinement costs to $5–$10 per cycle, significantly improving efficiency over expert hours.
Key Points
- EISR improves compliance decision rules through iterative error analysis.
- Achieved 79.6% accuracy with Python, outperforming at 72.7%.
- Auto-EISR costs $5–$10 per refinement cycle versus $70 expert-hours.
- Deployed for 22 days, processed 3,349 audit cases in logistics.
- Performance gains are significant in skewed-base-rate decision tasks.
Paper Resources
Source Excerpt
arXiv:2606. 10457v1 Announce Type: new Abstract: Decision rules that enterprise experts apply tacitly -- in auditing, compliance, and contract review -- can be systematically recovered and improved through iterative error analysis.
We present \textbf{Trace2Policy}, whose core mechanism -- \textbf{EISR} (\textbf{E}rror-driven \textbf{I}terative \textbf{S}kill \textbf{R}efinement) -- maintains a human-readable rule document as its optimization target: each round executes the rules on a validation set, clusters errors by root cause into MISSING, WRONG, or CONFLICT types, applies targeted patches, and commits only those that pass a regression gate. …
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